Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Maffei, Chiara; Girard, Gabriel; Schilling, Kurt G.; Aydogan, Dogu Baran; Adluru, Nagesh; Zhylka, Andrey; Wu, Ye; Mancini, Matteo; Hamamcı, Andaç; Sarica, Alessia; Teillac, Achille; Baete, Steven H.; Karimi, Davood; Yeh, Fang‐Cheng; Yildiz, Mert E.; Gholipour, Ali; Bihan-Poudec, Yann; Hiba, Bassem; Quattrone, Andrea; Quattrone, Aldo; Boshkovski, Tommy; Stikov, Nikola; Yap, Pew-Thian; Luca, Alberto De; Pluim, Josien P. W.; Leemans, Alexander; Prabhakaran, Vivek; Bendlin, Barbara B.; Alexander, Andrew L.; Landman, Bennett A.; Canales‐Rodríguez, Erick J.; Baraković, Muhamed; Rafael-Patiño, Jonathan; Yu, Thomas; Rensonnet, Gaëtan; Schiavi, Simona; Daducci, Alessandro; Pizzolato, Marco; Fischi-Gómez, Elda; Thiran, Jean‐Philippe; Dai, George; Grisot, Giorgia; Lazovski, Nikola; Puch, Santi; Ramos, Marc; Rodrigues, Paulo; Prčkovska, V.; Jones, Robert J.; Lehman, Julia; Haber, Suzanne N.; Yendiki, Anastasia

doi:10.1016/j.neuroimage.2022.119327

Cited by 22 publications

(15 citation statements)

References 80 publications

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“…Due to differences in fiber configuration in gray and white matters, and the folded geometry of the brain, tractography algorithms also tend to be biased towards terminating the streamlines at gyral crowns (Reveley et al, 2015). Overall, tractograms contain large amounts of false positives and false negatives that have been shown in several previous validation studies and tractography competitions (Thomas et al, 2014;Maier-Hein et al, 2017;Schilling et al, 2019;Girard et al, 2020;Maffei et al, 2020Maffei et al, , 2022. While the aforementioned limitations impact the quality of tractography, we believe that our techniques represent a significant progress in tractography-assisted neuronavigation, proposing a solution that shows the brain's anatomical connections in a way that is most accurate and helpful for brain stimulation, especially for TMS applications.…”

Section: Limitations Of Tractographymentioning

confidence: 94%

“…harvard.edu/) show that fiber configurations that go beyond crossing, e.g., fanning, branching, can be more challenging for tractography (Maffei et al, 2020(Maffei et al, , 2022Schilling et al, 2022). Due to differences in fiber configuration in gray and white matters, and the folded geometry of the brain, tractography algorithms also tend to be biased towards terminating the streamlines at gyral crowns (Reveley et al, 2015).…”

Section: Limitations Of Tractographymentioning

confidence: 99%

“…However, introducing real-time structural connectivity estimation to existing nTMS systems is challenging. This is mainly due to the inherent limitations involved with the accuracy of tractography, which have been increasingly pointed out in recent years by validation studies (Thomas et al, 2014;Yendiki et al, 2022) and benchmarks conducted through international tractography challenges (Maier-Hein et al, 2017;Nath et al, 2020;Schilling et al, 2021;Maffei et al, 2022). Importantly, tractography is well-known to miss connections that are present in the brain, i.e., false negatives , and at the same time, it generates connections that do not exist, i.e., false positives (Schilling et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Real-time tractography-assisted neuronavigation for TMS

Aydogan

Souza

Matsuda

et al. 2023

Preprint

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Background: State-of-the-art navigated transcranial magnetic stimulation (nTMS) systems can display the TMS coil position relative to the structural magnetic resonance image (MRI) of the subject's brain and calculate the induced electric field. However, the local effect of TMS propagates via the white-matter network to different areas of the brain, and currently there is no commercial or research neuronavigation system that can highlight in real time the brain's structural connections during TMS. Objective: To develop a real-time tractography-assisted TMS neuronavigation system and investigate its feasibility. Method: We propose a modular framework that seamlessly integrates offline (preparatory) analysis of diffusion MRI data with online (real-time) tractography. For tractography and neuronavigation we combine our custom software Trekker and InVesalius, respectively. We evaluate the feasibility of our system by comparing online and offline tractography results in terms of streamline count and their overlap. Results: A real-time tractography-assisted TMS neuronavigation system is developed. Key features include the application of state-of-the-art tractography practices, the ability to tune tractography parameters on the fly, and the display of thousands of new streamlines every few seconds using a novel uncertainty visualization technique. We demonstrate in a video the feasibility and quantitatively show the agreement with offline filtered streamlines. Conclusion: Real-time tractography-assisted TMS neuronavigation is feasible. With our system, it is possible to target specific brain regions based on their structural connectivity, and to aim for the fiber tracts that make up the brain's networks.

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Section: Limitations Of Tractographymentioning

confidence: 94%

Section: Limitations Of Tractographymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-time tractography-assisted neuronavigation for TMS

Aydogan

Souza

Matsuda

et al. 2023

Preprint

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“…Diffusion MR tractography has frequently given implausible interpretations in tractography [Jones et al, 2013;Thomas et al, 2014;Schilling et al, 2018;Grisot et al, 2021;Maffei et al, 2022]. Accordingly, the tractography is considered to have limited utility to accurately reconstruct white matter pathways [Jones et al, 2013;Thomas et al, 2014;Maffei et al, 2022;Yendiki et al, 2022].…”

Section: Diffusion Mr Tractography and Its Limitationsmentioning

confidence: 99%

Mapping Human Brain Pathways: Challenges and Opportunities in the Integration of Scales

Charvet

2023

Brain Behav Evol

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The human brain is composed of a complex web of pathways. Diffusion magnetic resonance (MR) tractography relies on the principle of diffusion to reconstruct brain pathways. Its tractography is broadly applicable to a range of problems as it is amenable to being studied in individuals of any age and from any species. However, it is well-known that this technique can generate biologically implausible pathways, especially in regions of the brain where multiple fibers cross. This review highlights potential mis-connections in two cortico-cortical association pathways with a focus on the aslant tract and inferior frontal occipital fasciculus. There is a lack of alternative methods to validate observations from diffusion MR tractography, which emphasize the need to develop new integrative approaches to trace human brain pathways. This review discusses integrative approaches in neuroimaging, anatomical, and transcriptional variation as having much potential to trace pathways and map modifications in the evolution of human brain pathways.

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“…These studies provide the foundation for understanding the organization of white-matter pathways and for assessing the accuracy of pathways reconstructed by non-invasive neuroimaging in humans (Haber et al, 2023; Safadi et al, 2018; Jbabdi et al, 2013; Haber et al, 2022). In particular, the comparison of tracer injections to diffusion MRI (dMRI) tractography in the same NHP brain has generated important insights, e.g., on the fiber configurations that confound dMRI and on how dMRI data should be acquired and analyzed to maximize the accuracy of pathways reconstructed by tractography (Grisot et al, 2021; Maffei et al, 2022; Schilling et al, 2019; Yendiki et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data

Sundaresan,

Lehman,

Maffei

et al. 2023

Preprint

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Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deep-learning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of∼0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available athttps://github.com/v-sundaresan/fiberbundle_seg_tracing.

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Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Cited by 22 publications

References 80 publications

Real-time tractography-assisted neuronavigation for TMS

Real-time tractography-assisted neuronavigation for TMS

Mapping Human Brain Pathways: Challenges and Opportunities in the Integration of Scales

Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data

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